Hyungsik Roger Moon, Martin Weidner
arXiv 1 May 2026 · Econometrics
arXiv:2605.00614 · PDF · Extracted main text
In this paper we study the least squares (LS) estimator in a linear panel regression model with unknown number of factors appearing as interactive fixed effects. Assuming that the number of factors used in estimation is larger than the true number of factors in the data, we establish the limiting distribution of the LS estimator for the regression coefficients as the number of time periods and the number of cross-sectional units jointly go to infinity. The main result of the paper is that under certain assumptions the limiting distribution of the LS estimator is independent of the number of factors used in the estimation, as long as this number is not underestimated. The important practical implication of this result is that for inference on the regression coefficients one does not necessarily need to estimate the number of interactive fixed effects consistently.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Onatski, A (2010) Determining the number of factors from empirical distribution of eigenvalues | 1.000 | 6 | 3 | 100% |
| 2 | Bai, J. and Ng, S (2002) Determining the number of factors in approximate factor models | 0.928 | 4 | 3 | 100% |
| 3 | Bai, J (2009) Panel data models with interactive fixed effects | 0.874 | 36 | 11 | 67% |
| 4 | Kim, D. and Oka, T (2014) Divorce law reforms and divorce rates in the usa: An interactive fixed-effects approach | 0.874 | 9 | 2 | 100% |
| 5 | Moon, H. and Weidner, M (2013) Dynamic Linear Panel Regression Models with Interactive Fixed Effects self | 0.839 | 22 | 8 | 59% |
| 6 | Ahn, S. C. and Horenstein, A. R (2013) Eigenvalue ratio test for the number of factors | 0.737 | 3 | 2 | 100% |
| 7 | Bai, J (2009) Panel data models with interactive fixed effects | 0.737 | 3 | 2 | 100% |
| 8 | Pesaran, M. H (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure | 0.737 | 3 | 2 | 100% |
| 9 | Wolfers, J (2006) Did unilateral divorce laws raise divorce rates? a reconciliation and new results | 0.693 | 7 | 1 | 100% |
| 10 | Ahn, S. C., Lee, Y. H., and Schmidt, P (2001) GMM estimation of linear panel data models with time-varying individual effects | 0.644 | 3 | 2 | 67% |
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